System and method for utilizing shape analysis to assess fetal abnormality

ABSTRACT

A method and system for utilizing shape analysis to assess fetal abnormality. According to one embodiment, coordinates of points identifying a shape in a fetal image are received, coefficients of one or more mathematical functions that describe the identified shape are determined, and the determined coefficients are utilized as markers to assess fetal abnormality.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. § 119(e) of U.S.Provisional Application No. 60/490,540, filed Jul. 29, 2003 and U.S.Provisional Application No. 60/493,442, filed Aug. 8, 2003, both ofwhich are hereby incorporated by reference as if repeated herein intheir entirety.

BACKGROUND OF THE INVENTION

Prenatal screening methods are routinely employed to assess thelikelihood of fetal abnormalities, commonly referred to as birthdefects. For example, Down syndrome or Trisomy 21 is the most commoncause of severe learning disability and accounts for approximately onehalf of all chromosomal anomalies in live born children.

Current methods to screen prenatally for trisomy 21 involve maternalserum testing for biochemical markers and/or ultrasound evaluation ofbiophysical markers. Maternal serum screening involves the quantitativeanalysis of biochemical markers and risk assessment based on likelihoodratios derived from the population distributions of affected andunaffected pregnancies. Ultrasound evaluation, however, has historicallyinvolved visual observation of a fetal image and deciding empiricallywhether the image looks “normal” or “abnormal” (for example, whether thecerebellum appears as a banana sign for open spina bifida). Thisapproach requires extensive experience in the “art” of ultrasound andthe interpretation is necessarily subjective.

Accordingly, there is a need in the art for a system and method thatadequately evaluates the morphological changes observed with birthdefects during prenatal screening.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide for utilizing shapeanalysis to assess fetal abnormality. According to one embodiment,coordinates of points identifying a shape in a fetal image are received,coefficients of one or more mathematical functions that describe theidentified shape are determined, and the determined coefficients areutilized as markers to assess fetal normality.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart that depicts a process for utilizing shapeanalysis to assess fetal abnormality in accordance with an embodiment ofthe present invention.

FIG. 2 is a flow chart that depicts a process for utilizing shapeanalysis to assess fetal abnormality in accordance with an embodiment ofthe present invention.

FIG. 3 is a block diagram that depicts a user computing device inaccordance with an embodiment of the present invention.

FIG. 4 is a block diagram that depicts a network architecture inaccordance with an embodiment of the present invention.

FIG. 5 is a flow chart that depicts a process for utilizing shapeanalysis of a fetal head to determine risk of fetal abnormality inaccordance with an embodiment of the present invention.

FIG. 6 is a screen shot that depicts outlining of a fetal head inaccordance with an embodiment of the present invention.

FIG. 7 is a screen shot that depicts outlining of a fetal head inaccordance with an embodiment of the present invention.

FIG. 8 is a flow chart that depicts a process for utilizing shapeanalysis of a fetal brow to determine risk of fetal abnormality inaccordance with an embodiment of the present invention.

FIG. 9 is a screen shot that depicts outlining of a fetal brow inaccordance with an embodiment of the present invention.

FIG. 10 is a screen shot that depicts outlining of a fetal brow inaccordance with an embodiment of the present invention.

FIG. 11 is a screen shot that depicts outlining of a fetal brow inaccordance with an embodiment of the present invention.

FIG. 12 is a screen shot that depicts outlining of a fetal brow inaccordance with an embodiment of the present invention.

DETAILED DESCRIPTION Overview

FIG. 1 depicts a process for utilizing shape analysis to assess fetalabnormality in accordance with an embodiment of the present invention.Upon receiving coordinates of points identifying a shape in a fetalimage (step 100), the coordinates are used to determine coefficients ofa mathematical function or functions that describe the identified shape(step 110). These coefficients are used as markers to assess fetalabnormality (step 120).

As shown in FIG. 2, once the coefficient markers are determined (step200), they may be used by themselves or with other markers to assessfetal abnormality (step 210). A fetal abnormality may be assessed bycomparing a patient's coefficient markers to reference data ofcoefficient markers by conducting a statistical analysis. The referencedata may contain unaffected patients and/or affected patients. Thestatistical comparison could result in a risk of fetal abnormality, alikelihood ratio for a fetal abnormality or an index value that could beconsidered within range or outside of range for a fetal abnormality.

The use of multidimensional coordinates allows for the evaluation of ashape as a whole. In one embodiment, a statistical shape analysisinvolves the tracing of an outline around the part of a fetal image tobe analyzed. The points that make up this curve are then analyzed toderive a function that best fits the individualized points around theoutline. The coefficients of this function may be considered randomvariables and can be determined for each evaluated image. Thecoefficients may then be analyzed using multivariate statistics todetermine if they are outliers compared to the normal population. Asubject shape that has coefficients outside the normal ranges observedin control shapes would indicate that the subject shape wassignificantly different than expected.

Described below are several embodiments within which the presentinvention may be implemented.

Architecture

FIGS. 3 and 4 illustrate the components of a basic computer and networkarchitecture in accordance with an embodiment of the present invention.FIG. 3 depicts user computing device 300, which may be an ultrasoundmachine (3-D, 4-D or color), MRI or CAT scan machine, fetoscopy machine,workstation, personal computer, handheld personal digital assistant(“PDA”), or any other type of microprocessor-based device. Usercomputing device 300 may include a processor 310, input device 320,output device 330, storage device 340, client software 350, andcommunication device 360.

Input device 320 may include a keyboard, mouse, pen-operated touchscreen or monitor, voice-recognition device, or any other device thataccepts input. Output device 330 may include a monitor, printer, diskdrive, speakers, or any other device that provides output.

Storage device 340 may include volatile and nonvolatile data storage,including one or more electrical, magnetic or optical memories such as aRAM, cache, hard drive, CD-ROM drive, tape drive or removable storagedisk. Communication device 360 may include a modem, network interfacecard, or any other device capable of transmitting and receiving signalsover a network. The components of user computing device 300 may beconnected via an electrical bus or wirelessly.

Client software 350 may be stored in storage device 340 and executed byprocessor 310, and may include, for example, imaging and analysissoftware that embodies the functionality of the present invention.

FIG. 4 illustrates a network architecture in accordance with anembodiment of the present invention. The network architecture allows theimaging and analysis functionality of the present invention to beimplemented on more than one user computing device 300. For example, inone embodiment user computing device 300 may be an ultrasound machinethat performs all of the imaging and analysis functionality of thepresent invention. In another embodiment, user computing device 300 amay be an ultrasound machine that performs the imaging functionality ofthe present invention, and then transfers image or coordinate data overnetwork 410 to server 420 or user computing device 300 b or 300 c foranalysis of the data. The analyzed data could further be transferred toanother user computing device 300 belonging to the patient or anothermedical services provider for testing with others markers.

Network link 415 may include telephone lines, DSL, cable networks, T1 orT3 lines, wireless network connections, or any other arrangement thatimplements the transmission and reception of network signals. Network410 may include any type of interconnected communication system, and mayimplement any communications protocol, which may secured by any securityprotocol.

Server 420 includes a processor and memory for executing programinstructions, as well as a network interface, and may include acollection of servers. In one particular embodiment, server 420 mayinclude a combination of servers such as an application server and adatabase server. Database 440 may represent a relational or objectdatabase, and may be accessed via server 420.

User computing device 300 and server 420 may implement any operatingsystem, such as Windows or UNIX. Client software 350 and server software430 may be written in any programming language, such as ABAP, C, C++,Java or Visual Basic.

Analysis of Fetal Head Shape Embodiment

FIG. 5 provides an example embodiment of the present invention in whichthe shape of a fetal head is analyzed to assess fetal abnormality. Fetalabnormalities identifiable through the use of the present invention mayinclude, among others, Down syndrome, Spina Bifida, Trisomy 18, Trisomy13, frontal bossing, unbalanced translocation, other chromosomalabnormalities, heart abnormalities and abnormalities of any major bodyorgan, structural abnormalities and craniofacial abnormalities.

According to this embodiment, a transverse view of the fetal head isobtained by ultrasound (e.g., UCD 300 a), saved as a bit-map image andtransferred to another computer (e.g., UCD 300 b). Then, in step 500, auser (e.g., user 400 b) employs digitizing software (e.g., clientsoftware 350), such as TPSDIG, DigitX, CalExcel, DSDigit, Digical,Windig or MacMorph, to create an outline of the fetal head to beanalyzed. (Digitizing software provides coordinate data when a userclicks on a particular point in a bit-map image.) In creating theoutline, the user may identify two landmarks on the bit-map image alongthe OFD (ocipito-frontal diameter) axis so that the image may be alignedagainst a consistent axis and to allow for a uniform assessment ofpoints on each image.

As shown in FIG. 6, prior to placing the coordinates on the outline, aseries of 20 lines, each the length of the OFD axis, may be generatedutilizing software employing general algorithms. The first line overlaysthe OFD axis; the remaining lines are centered at the mid-point of thefirst line and then are rotated at 9 degrees from the previous line in acounterclockwise manner. Once this “fan” is drawn, the user may placepoints, via the digitizing software, on the outline of the fetal headwhere it crosses the equidistant lines of the fan. FIG. 7 shows theresulting 40 points representing the outline. Each small circleconnected in the loop represents a point on the image clicked by theuser to identify the outline, and the two cross-hair symbols next to thenumber “2”s represent the OFD positioning landmarks. The use of the“fan” allows for more consistency in creating the outlines on differentimages.

In step 510, the user-identified outline points are converted into datacoordinates by the digitizing software. TABLE 1 represents the landmarksand outline points of FIG. 7 as stored in an output file. The outputfile lists the coordinate data in a tabular format, with thex-coordinate listed first followed by the y-coordinate. As indicated inTABLE 1, the two landmark coordinates are listed first, followed by the40 point coordinates. TABLE 1 LM = 2 401.00000 304.00000 180.00000347.00000 CURVES = 1 POINTS = 40 401.00000 302.00000 399.00000 322.00000393.00000 337.00000 386.00000 353.00000 375.00000 365.00000 365.00000376.00000 356.00000 386.00000 345.00000 395.00000 334.00000 400.00000320.00000 402.00000 307.00000 402.00000 294.00000 401.00000 283.00000400.00000 271.00000 397.00000 260.00000 396.00000 247.00000 391.00000236.00000 387.00000 220.00000 381.00000 209.00000 373.00000 194.00000364.00000 182.00000 349.00000 178.00000 330.00000 183.00000 313.00000189.00000 297.00000 198.00000 283.00000 207.00000 270.00000 221.00000261.00000 236.00000 254.00000 248.00000 248.00000 260.00000 243.00000272.00000 240.00000 287.00000 236.00000 301.00000 236.00000 315.00000235.00000 331.00000 238.00000 346.00000 244.00000 359.00000 252.00000371.00000 264.00000 382.00000 275.00000 391.00000 288.00000 IMAGE =Image1.bmp ID = 1 SCALE = 0.140234

In order to ensure the coordinate data is aligned properly with the twospecified landmarks, the user may employ a software program thatutilizes general algorithms to adjust the coordinate data by rotatingand translating each data point so that the OFD axis lies along thehorizontal axis and that the first point lies at the origin,(x,y)=(0,0). These adjustments do not change the shape of the outlinebeing investigated. The software program may employ the followinggeneral algorithm:

-   -   Point 1=Origin: Denote as X₁,Y₁    -   Point 2=Other end of OFD axis: Denote as X₂,Y₂    -   Determine Slope of Line: (Y₂−Y₁)/(X₂−X₁)    -   Determine the angle of the slope: Theta=arctangent(Slope)    -   For each point in the shape rotate the point clockwise:        -   Subtract out the origin such that NewX=X−X₁, NewY=Y−Y₁        -   FinalX=NewX*cos(Theta)+NewY*sin(Theta)        -   FinalY=newY*cos(theta)−NewX*sin(theta)

The set of FinalX, FinalY values represent the rotated data points suchthat the OFD axis lies horizontally. In different scenarios, it may beappropriate to rotate the image in a counter clockwise manner; if thisis the case then the formulas for FinaIX and FinalY are:

-   -   FinalX=NewX*cos(Theta)−NewY*sin(Theta)    -   FinalY=NewX*sin(Theta)+NewY*cos(Theta)

In step 520, the user may employ software such as the NTSYSPC program(Exeter Software), which uses an elliptical Fourier analysis program togenerate a series of best fit curves to the coordinate data usingharmonics. The zero harmonic consists of only 2 coefficients andrepresents only translation (i.e. movement in the x and y direction) andnot shape itself so these 2 coefficients are usually not analyzed. Eachharmonic comprises four coefficients of interpolation functions thatdescribe the user-identified outline shape. In general, the moreharmonics used, the better the fit to the coordinates; however, if toomany harmonics are used the fit may be too good since small differencesmight appear due to poor placement of the outline point during step 500.In this embodiment, the user evaluates the first harmonic (i.e., thefour coefficients a1, b1, c1 and d1).

The user may choose alternatives to the method described above foraligning the coordinate data. For example, the user could rotate theimage so that the long axis of the shape described by the first harmonicis parallel to the x-axis, or the user can align the image such that thefirst point of the outline is set equal to the end point of the firstharmonic to the x-axis, or the user could combine both of these methods.These options are available as part of the Elliptical Fourier Analysismodule in the NTSYSPC program.

In step 530, the determined coefficients may be utilized as markers toassess fetal abnormality by conducting a statistical analysis to comparethe patient's determined coefficients with reference parameters derivedfrom reference data of coefficient markers (e.g., a statisticaldistribution of determined coefficients in the unaffected populationand/or affected population). One exemplary method of doing this is tocalculate the Mahalanobis Squared Distance (MSD) value for the patient'scoefficients.

Prior to determining the outline coefficients, the coordinates may beadjusted by scaling the coordinates so that the area enclosed inside theoutline equals 1. The original areas of the enclosed outline could beincluded as a separate variable in addition to the coefficients as partof the statistical comparison analysis. Also, many shapes change withgrowth in the fetus. As a result, it may be necessary to adjust theobserved coefficients to account for gestational age of the fetus aspart of the statistical analysis.

Several other shape analysis methods besides elliptical Fourier analysismay be utilized by the present invention. All of the following methodsdetermine coefficients of functions that may be used as markers toassess fetal abnormality. Some examples of outline methods are:

-   -   A. polynomials    -   B. cubic splines    -   C. parametric polynomials    -   D. parametric cubic splines    -   E. bezier curves    -   F. Fourier analysis of equally spaced radii    -   G. dual axis Fourier analysis

All of these methods deal with outlines, of which there are fourclasses—simple open outlines, simple closed outlines, complex openoutlines, complex closed outlines. A simple open outline is an openoutline that has only one value of y for each x. A complex open outlineis an outline that can have more than one y value for each x. Simpleclosed outlines are closed outlines such that if one draws a line fromthe center through the outline it only crosses the outline once whereasin a complex outline the line would cross the outline more than once. Inmost cases an open outline can be analyzed as if it is a closed outlineby assuming there is a straight line from the last to first point of theoutline or by mirroring all of the coordinates around the x (or y) axis,however the outline can be analyzed as an open outline. Some of themethods above, such as the polynomial method, work with open outlineswhile others like the elliptical Fourier analysis work with closedoutlines. It is also possible to analyze 3-dimensional outlines (x,y,z)in accordance with an alternative embodiment of the present invention.

To provide the reference data to which the determined coefficients arecompared in step 530, a statistical algorithm may be utilized todetermine the statistical distribution of the coefficients in theunaffected population using a set of coefficients for a series ofunaffected pregnancies. The distribution may be defined by a series ofreference parameters for each coefficient or pair of coefficients suchas means, standard deviations and correlations. The coefficientsassociated with an outline in a particular patient could be compared tothese reference parameters and the chance that the coefficients could beequal to or more extreme than their observed values could be determined.

If affected cases are available then they may be included in thereference data set to determine the statistical distribution of thecoefficients in the affected population using a set of coefficients froma series of affected pregnancies. The patient's coefficients could thenbe compared to the distribution of coefficients in the affectedpopulation. Alternatively, the patient's coefficients could be comparedto both the unaffected distribution and the affected distribution asdefined by the reference parameters. For example, if both the unaffectedcases and affected cases are multivariately normally distributed, thedistribution function for the multivariate normal distribution can beused with the reference parameters for the unaffected distribution todetermine a relative frequency for the unaffected distribution and thenagain for the affected distribution to determine a relative frequencyfor the affected distribution, and then a likelihood ratio may bedetermined. A likelihood ratio equals the quotient of the relativefrequency in the affected distribution to the relative frequency in theunaffected distribution. A risk result (e.g., 1 in 100) gives the chancethat a patient with the same parameters could have a child with a fetalabnormality. A likelihood ratio gives the relative risk that the patientcould have a child with a fetal abnormality.

The likelihood ratio can be used to multiply a prior risk to determine aposterior risk (after accounting for the minor adjustment between oddsand risk, if necessary). For example, the prior risk of Down syndrome isoften based on maternal age. If the statistical distribution of thedetermined coefficients are independent of the distribution of othermarkers, the likelihood ratio could also be used to adjust the risk ofDown syndrome determined by the other markers to determine the overallrisk of Down syndrome based on the outline and the other markers.Alternatively, if the coefficients are not independent of the othermarkers, then risk of Down syndrome could be determined by utilizingreference parameters for a combination of the coefficients and the othermarkers together using multivariate normal distributions or otherdistribution functions.

Examples of other markers include nuchal translucency, free Beta hCG andPAPP-A, Ductus Venosus, absent or hypoplastic nasal bone observed onultrasound, maternal blood alpha-fetoprotein, maternal blood hCG,maternal blood unconjugated estriol, maternal blood dimeric inhibin A,maternal urine total estriol, maternal urine beta core fragment,maternal urine hyperglycosylated hCG, maternal blood hyperglycosylatedhCG, ultrasound “soft markers” which include for example, nuchal edemaor increased nuchal fold, short femur, hyperechogenic bowel, andechogenic foci in the heart, etc.

As mentioned above, the other markers can be combined statistically withthe results from the shape analysis to provide a final result to thepatient. Alternatively, the medical tests for the other markers could beperformed prior to the ultrasound exam and then, when the ultrasoundexam is completed, the results of the other marker tests can bethereafter combined with the results from the shape analysis.

For providing reference data according to this embodiment of the presentinvention, columns 2-5 of TABLE 2A show the results of evaluating thefirst harmonic (i.e., the four coefficients a1, b1, c1 and d1) of fetalhead outlines in a study of 35 unaffected pregnancies in the firsttrimester. TABLE 2B lists the reference parameters consisting of themean and standard deviation of each coefficient and thevariance/covariance matrix consisting of the variance (standarddeviation squared) and covariance between each pair of coefficients andthe formula for calculating a Mahalanobis-Squared Distance (MSD) foreach case. TABLE 2A ID a1 b1 c1 d1 1 .618923 −.021438 .017238 .512385 2.618007 −.00636 .004269 .513899 3 .611512 −.009105 .013914 .519218 4.620708 .001494 .005816 .511672 5 .620591 −.001878 .002653 .511512 6.610873 −.005378 .009693 .520104 7 .626065 .002194 .00969 .506797 8.610239 −.022687 .015904 .520122 9 .619487 −.009327 .000085 .512371 10.61309 .002649 .013512 .518273 11 .610239 .009847 −.00302 .520935 12.633193 −.0167 .021139 .500202 13 .617141 −.031007 .009402 .513915 14.613927 −.003108 .020587 .517351 15 .624268 .010912 .002663 .508347 16.620115 −.027663 .02827 .510862 17 .613857 −.034757 .023583 .516377 18.636496 −.010523 .021077 .497571 19 .607668 −.026101 .003049 .522709 20.614509 .004114 .020143 .517041 21 .627318 −.034898 .014111 .50485 22.605777 −.018182 .013938 .524513 23 .606093 .009761 .007094 .524719 24.633498 −.005676 .029437 .500312 25 .634393 −.00556 .013881 .499904 26.606259 .006939 −.016111 .52415 27 .63582 −.031021 .035875 .497006 28.607238 −.040385 .006671 .522681 29 .612626 −.023636 −.006856 .518649 30.6171 .026204 −.001128 .514461 31 .640095 −.022288 .018631 .494486 32.625569 −.012373 .038643 .506998 33 .611093 −.02788 .032891 .518468 34.629932 −.03224 .033393 .501638 35 .608294 .041921 −.008744 .522055

TABLE 2B Variable Mean Std. Dev. a1 −.6189147 .0099677 b1 .0104039.0183979 c1 −.0128969 .0130582 d1 −.5127587 .00881 Variance/CovarianceMatrix (M) a1 b1 c1 d1 a1 .000099 −.000035 .000064 −.000088 b1 −.000035.000338 −.000125 .000036 c1 .000064 −.000125 .00071 −.00006 d1 −.000088.000036 −.00006 .000078MSD=(X-μ)^(T) M⁻¹ (X-μ)

-   -   where:        -   (X-μ) is a 4 element vector consisting of the patient's 4            coefficients (a1, b1, c1, c1 minus their respective            reference means).        -   (X-μ)^(T) is the transpose of the (X-μ) vector        -   M⁻¹ is the Inverse of the 4×4 Variance/Covariance Matrix

TABLE 2C shows the MSD calculation in the 35 unaffected cases. A cut-offbeyond 95% of the observed data was established representing a pointhalfway between the last 2 MSD values (11.714). TABLE 2D shows theresults of 2 patients who happened to be carrying a fetus with Downsyndrome, both of which based on their MSD calculation are outliers. Asmore data from Down syndrome pregnancies are gathered, additionalreference parameters (e.g., means, standard deviations, and covariances)based on the Down syndrome cases could be calculated along with otherstatistical techniques such as likelihood ratios to determine the oddsthat a patient is carrying a fetus with Down syndrome. TABLE 2C ID MSD 1.5453869 2 .72770054 3 .86797516 4 .99110505 5 1.0513294 6 1.1757276 71.3182192 8 1.639374 9 1.7719449 10 1.9632445 11 2.0438778 12 2.090437713 2.4440608 14 2.5725717 15 2.6427427 16 2.8276119 17 3.255354 183.5018765 19 3.6411678 20 3.7887796 21 3.9182202 22 4.0570078 234.5657224 24 4.6067905 25 4.9834947 26 5.5061446 27 5.5437792 285.877155 29 6.0931487 30 6.2047977 31 6.3209851 32 6.5548075 337.4795723 34 9.0892238 35 14.338667

TABLE 2D Outcome a1 b1 c1 d1 MSD 36 .570043 −.015975 .01023 .55819194.220565 37 .58333 .076813 −.056937 .538497 403.86348

In this embodiment of the present invention, only the data associatedwith TABLE 2B would have to be stored in the computing device for thestatistical comparison analysis of a particular patient to be conducted.This would preserve storage resources in the event of reference databased on very large populations.

Analysis of Fetal Brow Embodiment

FIG. 8 provides an example embodiment of the present invention in whichthe shape of a fetal brow is analyzed using 3-D ultrasound to assessfetal abnormality. In traditional 2-D ultrasound the challenge to thesonographer is obtaining an image that is in the proper plane of view.The ability to consistently obtain the same angle and depth of view ofthe fetus requires subjective decision making during the ultrasoundexam. Thus it is difficult to obtain consistent views of each fetus fromexam to exam. This increases error when trying to analyze shapes fromthese images.

3D ultrasound allows for the simultaneous visualization of the fetus in3 separate 2-D planes, as shown in FIG. 9. These three planes are calledthe sagittal (side-view), coronal (front-view) and transverse(top-view). In FIG. 9, the top left image represents the transverseplane, the top right represents the coronal plane, and the bottom rightrepresents the sagittal plane.

Misalignment of an image in any given plane distorts the images in theother two planes. Therefore, in order to perform proper 2-D imageanalysis of any given plane, the image should first be aligned properlyin all three planes. For example, to analyze an image in the sagittalplane, a user can assure a proper view by aligning the fetus in thecoronal and transverse views. In addition, by choosing a landmark ineach plane (e.g. the white dot placed at the bridge of the nose in theFIG. 9) the depth of the section can also be defined. Using landmarksand aligning the fetus in all three planes can insure that the view ofeach fetus in any given plane is the same and thus reduce variation inshape due to differences in ultrasound technique. Ultimately this willimprove the ability to see changes due to biological effects. 3Dultrasound further allows for image manipulation after the completion ofthe examination. Therefore, after capturing a 3-D image the operator canlater rotate that image to the appropriate view.

The use of 3D sonography is a recent advance in prenatal ultrasound. Thetechnique generates a multiplanar display of separate images in thecoronal, sagittal and transverse planes obtained by the ascertainment ofa single “volume”. Once the volume is obtained the images in each planemay be rotated to provide consistent and reproducible planes as part ofa “post processing” evaluation. This advance was previously unobtainableusing conventional 2D sonographic techniques. Once a given desired imageand plane is obtained, it may be superimposed on a digitized screenwhere geometric morphometric analysis may be performed.

According to this embodiment, a fetal brow is analyzed in the sagittalplane—from the bridge of the nose to the midportion of the top of theskull. The fetal image of FIG. 9 is obtained by 3-D ultrasound (e.g.,UCD 300 a), saved as a bit-map image and transferred to another computer(e.g., UCD 300 b). The sonographer (e.g., user 400 a) places thelandmark at the bridge of the nose in each plane.

In step 800, a user (e.g., user 400 b) employs digitizing software tocreate an outline of the fetal brow to be analyzed. As illustrated inFIG. 10, the user first places two landmark points on the fetal head inthe sagittal plane—one at the bridge of the nose and the other at themidportion of the top of the skull. Once these landmarks are correctlyin place, the user employs a software program that utilizes a generalalgorithm to find a point that has the same horizontal component as thelandmark on the bridge of the nose, and the same vertical component asthe landmark at the top of the head. Once this point is found, a seriesof 16 lines of equal length are drawn, as shown in FIG. 11.

The first line in FIG. 11 starts from the center point and goes throughthe landmark at the bridge of the nose. The remaining lines start at thecenter point and then are rotated at 6 degrees from the previous line ina counterclockwise manner until a line is drawn through the point at thetop of the head which is at a 90 degree angle from the initial line.Once this “fan” is drawn, the user may place points on the outline ofthe skull where it crossed the equidistant lines of the “fan” using thedigitizing software. This method ensures the user to get curves withless human error and more reproducibility. FIG. 12 shows the 16resulting points placed in the sagittal plane.

In step 810, the user-identified outline points are converted into datacoordinates by the digitizing software. The user may choose to use onlythe first eight points in subsequent analysis since these points moredeterminatively represent the fetal brow.

In step 820, an elliptical Fourier analysis is employed using threeharmonics (i.e., three sets of coefficients). The determinedcoefficients are utilized as markers to assess fetal abnormality byconducting a statistical analysis, such as a principal component (PC)analysis.

In step 830, PC scores are determined based on a PC analysis of thedetermined Fourier coefficients, and in step 840, the MSD values of thePC scores are calculated. As discussed above, this shape analysis mayalso be combined with other markers to more completely assess fetalabnormality.

The PC analysis utilized by this embodiment is a statistical techniqueused with multivariate data to reduce the number of variables used infurther statistical analysis. (It can also be used as an exploratoryanalysis to see which of the variables are most important). The PCanalysis is a standard statistical technique that generates a set oflinear combinations of the underlying variables. These linearcombinations represent new variables that can be used in otherstatistical analyses. The first linear combination is the most importantvariable and so on. Higher numbered principal components can be droppedfrom further analyses since they tend to represent noise. Once theprinciple components are determined (e.g., 0.3X₁+0.2X₂+0.4X₃ . . . ),the PC scores can then be calculated which can be used as the variablesin an MSD calculation.

For providing reference data according to this embodiment of the presentinvention, TABLE 4 shows the results of evaluating the first threeharmonics (i.e., three sets of the four coefficients A, B, C and D) offetal brow outlines in a study of 32 normal pregnancies. TABLE 4 imageoutcome addnl_img a1 b1 c1 d1 a2 b2 c2 d2 a3 b3 c3 d3  1-4x2 0 0 −1.53−.0426 −.00609 −.205 .000102 .00658 .0751 −.00803 −.163 −.0141 .00464−.0148 865-16x2 0 0 −1.52 −.0427 .000763 −.204 −.000096 .00607 .071.000297 −.162 −.014 −.000216 −.0166  1-7x 0 0 −1.5 −.0434 .000701 −.208.00116 .00865 .0756 −.00421 −.161 −.0123 .0103 −.0123  1-27x2 0 0 −1.5−.0478 −.00868 −.209 .000064 .00832 .0771 −.0112 −.161 −.0163 .00577−.0116  1-5x2 0 0 −1.36 −.059 −.00291 −.233 −.000516 .0105 .0887 −.00506−.14 −.0194 .00354 −.0119  1-9x 0 0 −1.38 −.0487 .0112 −.225 −.000699.00801 .0785 .00928 −.145 −.0153 −.0000916 −.0167 865-1x2 0 0 −1.57−.0347 .0212 −.202 .00101 .00913 .0805 .0202 −.168 −.00939 −.00341−.00737  1-2x 0 0 −1.48 −.0377 .0133 −.215 .000543 .00961 .085 .0106−.158 −.0104 .00267 −.00535  2-20x2 0 0 −1.67 −.0407 −.0159 −.187−.0000525 .00626 .0685 −.0166 −.18 −.0141 .00452 −.014 865-10x2 0 0−1.27 −.0647 .00922 −.246 −.00123 .0109 .0872 .0062 −.13 −.02 .00275−.0167 865-15x2 0 0 −1.45 −.0362 .0204 −.218 −.00224 .00905 .082 .0209−.153 −.0119 −.00631 −.0124  1-12x1 0 0 −1.64 −.0383 −.00745 −.187.000816 .00495 .0604 −.0124 −.177 −.0124 .0108 −.0201  1-29x2 0 0 −1.44−.0539 −.00597 −.216 .00316 .00857 .0763 −.0134 −.152 −.0145 .0165−.0186 865-18x2 0 0 −1.81 −.0216 .00424 −.173 −.000255 .0042 .065 .00434−.198 −.00702 −.00122 −.00975 865-12x2 0 0 −1.54 −.0313 .015 −.205−.00311 .00838 .0791 .0182 −.165 −.0119 −.0111 −.01  1-6x2 0 0 −1.5−.0455 −.00627 −.21 −.0002 .00953 .0831 −.00785 −.162 −.0151 .00415−.00405 865-6x2 0 0 −1.66 −.0336 −.0104 −.191 −.000402 .00666 .0773−.0113 −.18 −.0119 .0032 −.00458 865-11x2 0 0 −1.39 −.0673 −.0116 −.224.00189 .0084 .0754 −.0158 −.143 −.0205 .00992 −.0251 865-7x2 0 0 −1.36−.0427 .0272 −.234 −.00137 .0128 .092 .0263 −.143 −.0123 −.00534 −.00475865-9x2 0 0 −1.87 −.0212 .00799 −.165 −.0012 .00491 .0558 .00881 −.204−.00773 −.0034 −.0178  1-30x2 0 0 −1.31 −.0799 −.0141 −.239 .0021 .0138.0851 −.0225 −.134 −.0235 .0205 −.0197  1-13x1 0 0 −1.3 −.0583 .014 −.24−.00241 .00913 .0805 .0123 −.131 −.0191 −.00154 −.025 865-3xx2 0 0 −1.99−.0176 .0217 −.156 −.00203 .00869 .0563 .0251 −.221 −.00685 −.0134−.00767 865-8x2 0 0 −1.9 −.0272 −.0168 −.164 .00109 .0053 .0601 −.0231−.209 −.00858 .0175 −.00902 865-14x2 0 0 −2.16 −.026 −.0209 −.142.000559 .00425 .0464 −.0211 −.238 −.00879 .00466 −.0176  2-18x2 0 0−1.98 −.0161 .0138 −.154 −.000513 .00284 .0467 .0112 −.215 −.00484.00322 −.0243  1-28x2 0 0 −1.39 −.0642 −.00215 −.22 .000798 .00732 .0643−.00749 −.144 −.02 .0106 −.035  2-19x2 0 0 −1.4 −.0598 .00756 −.232.00114 .016 .101 .00485 −.145 −.0183 .0018 .00274  1-1x 0 0 −1.14 −.0995.0115 −.272 −.000611 .0122 .0868 .0042 −.108 −.0301 .00714 −.0274 1-11x2 0 0 −1.19 −.111 −.0167 −.266 .000575 .0157 .0974 −.024 −.116−.0352 .0161 −.0141  1-14x 0 0 −1.62 −.028 .0307 −.201 −.00573 .014.0889 .0371 −.174 −.0128 −.0219 .00249  1-10x2 0 0 −1.18 −.0676 .0359−.271 −.00495 .0222 .102 .0335 −.12 −.0211 −.00789 −.00529

TABLE 5 shows the resulting PC analysis of the Fourier coefficients.TABLE 5 PRINCIPAL COMPONENT ANALYSIS pca a1-d3 if outcome == 0, &addnl_img == 0, mineigen(1.0) (obs = 32) (principal components; 3components retained) Component Eigenvalue Difference ProportionCumulative 1 5.96194 1.82595 0.4968 0.4968 2 4.13599 3.09719 0.34470.8415 3 1.03880 0.57715 0.0866 0.9281 4 0.46165 0.21530 0.0385 0.9665 50.24635 0.14543 0.0205 0.9871 6 0.10092 0.06229 0.0084 0.9955 7 0.038630.02910 0.0032 0.9987 8 0.00953 0.00396 0.0008 0.9995 9 0.00558 0.005200.0005 0.9999 10  0.00038 0.00022 0.0000 1.0000 11  0.00016 0.000100.0000 1.0000 12  0.00006 . 0.0000 1.0000 Scoring Coefficients Variable1 2 3 Mean Std. Dev. a1 0.39500 −0.01100 −0.08986 −1.53125 .2485798 b1−0.36639 0.18716 0.08292 −.04715 .0221123 c1 0.06718 0.44499 −0.25403.0037642 .0151056 d1 −0.40589 −0.01047 0.07023 −.2098125 .0330263 a2−0.03619 −0.41808 0.20309 −.000394 .0018714 b2 0.33650 0.17614 0.25352.0091534 .0040555 c2 0.36181 0.14020 0.32808 .0765344 .014202 d2 0.025930.46832 −0.22168 .0015418 .0170104 a3 0.39740 −0.02115 −0.11651 −.1625.0312441 b3 −0.36383 0.15894 0.08324 −.0149906 .0066018 c3 0.06250−0.46168 0.10331 .0026395 .0092017 d3 −0.01183 0.28823 0.78874 −.0135719.0085003

In this embodiment, the first 3 principal components are retained andused to calculate the associated PC variables. First a z-score for eachcoefficient is calculated by subtracting the mean of the coefficient andthen dividing the difference by the standard deviation of thecoefficient from TABLE 5. Each PC variable is then created bymultiplying the scoring coefficient for each (a1-d3) by the z-score foreach variable and then summing the 12 products, as shown in TABLE 6.TABLE 6 image outcome addnl⁻img PCA1 PCA2 PCA3  1-4x2 0 0 −.4907767−.8738552 .0987127 865-16x2 0 0 −.6085553 −.2783825 −.4990045  1-7x 0 0−.2317154 −.874534 .4810387  1-27x2 0 0 .0218282 −.9802006 .569997 1-5x2 0 0 1.670697 −.3829324 .4203361  1-9x 0 0 .6840906 .4666384−.7679284 865-1x2 0 0 −.6142922 1.508776 .3821437  1-2x 0 0 .0732714.8884591 .8576227  2-20x2 0 0 −1.432505 −1.382093 .4184196 865-10x2 0 02.309402 .2540324 −.5981961 865-15x2 0 0 .2062483 2.133314 −.6064944 1-12x1 0 0 −1.716844 −1.812474 −.44028  1-29x2 0 0 .3513637 −2.44019.2868603 865-18x2 0 0 −2.942221 .4870823 .2135515 865-12x2 0 0 −.50701652.407711 −.391572  1-6x2 0 0 .1698279 −.2595708 1.379145 865-6x2 0 0−1.33174 −.3922991 1.348731 865-11x2 0 0 1.119487 −2.558559 −.6279961865-7x2 0 0 1.386343 2.689931 .3561011 865-9x2 0 0 −3.317 .6986394−.8962799  1-30x2 0 0 2.653671 −3.064767 .526195  1-13x1 0 0 1.737869.6931306 −1.907204 865-3xx2 0 0 −3.596458 2.816385 −.203151 865-8x2 0 0−3.22584 −2.18013 1.356439 865-14x2 0 0 −4.802877 −1.925512 .2700403 2-18x2 0 0 −4.344046 .1713885 −1.659267  1-28x2 0 0 .7005095 −2.296043−2.21848  2-19x2 0 0 2.251043 .7969418 2.307821  1-1x 0 0 4.38609−.9528349 −1.857011  1-11x2 0 0 5.014206 −2.781211 .8650386  1-14x 0 0−.0283105 5.296726 .5011879  1-10x2 0 0 4.454249 4.126434 .0334842

The PC variables are used in an MSD calculation to create an atypicalityindex, as shown in TABLE 7. The PC variables are independent of eachother so the variance/covariance matrix is not needed. A cut-off ofMSD>7.81 (95^(th) percentile of the expected X² with 3 d.o.f.) was usedto define outliers. TABLE 7 Variable Obs Mean Std. Dev. PCA1 32−1.15e−08 2.441709 PCA2 32 −2.79e−09 2.033714 PCA3 32  2.44e−09 1.019216MSD=(PCA1/2.441709){circumflex over ( )}2+(PCA2/2.033714){circumflexover ( )}2+(PCA3/1.019216){circumflex over ( )}2

TABLE 8A shows the coefficients from an elliptical Fourier analysis for6 affected cases and 8 images that represent an additional image from apatient in the reference set. The MSD is calculated using the means andstandard deviations of the coefficients, the PC scoring coefficients andthe MSD formula for the 32 unaffected patients, the six affectedpatients and 8 additional images from patients in the group. The resultsare shown in TABLE 8B. The 3 outliers were all abnormal (trisomy 21,trisomy 18, and one fetus with multiple congenital anomalies). Twotrisomy 21 and one case with an unbalanced translocation were notoutliers. The affected cases were 1-3× Trisomy 18, 2-21×2 campomelicdysplasia, 2-23×2, 2-23×3, 2-23×4 (same patient) Trisomy 21, 865×2Trisomy 21, 865-5×2 Trisomy 21, 865-17×2 translocation. TABLE 8A imageoutcome addnl_img a1 b1 c1 d1 a2 b2 c2 d2 a3 b3 c3 d3 865-11xx2 0 1 −1.8−.0179 .0173 −.174 −.00083 .00425 .0647 .0179 −.196 −.00566 −.00494−.0109 865-13xx2 0 1 −1.55 −.0308 .0201 −.202 −.00266 .00797 .0725 .0213−.165 −.0111 −.00747 −.0183  1-12x2 0 1 −1.91 −.0263 −.0153 −.163−.000187 .00376 .0617 −.0147 −.209 −.00962 .0013 −.00838  1-14x2 0 1−1.25 −.053 .0353 −.25 −.00137 .0141 .0898 .0317 −.129 −.0138 −.00302−.0125  1-14x3 0 1 −1.19 −.0866 .0063 −.26 −.000359 .0105 .0853 .0000546−.116 −.0263 .00843 −.0262  1-12x3 0 1 −1.56 −.0535 −.021 −.204 .00453.0105 .079 −.0298 −.167 −.0141 .0235 −.00989  2-23x2 1 0 −1.43 −.0371.0264 −.223 −.00117 .0118 .0906 .0265 −.152 −.0107 −.00682 −.00278865-17x2 1 0 −1.34 −.0532 .00953 −.24 −.00186 .0136 .0975 .00848 −.138−.0172 −.0011 −.00345 865-5x2 1 0 −1.5 −.0568 −.018 −.208 .00261 .00753.0721 −.024 −.159 −.0172 .0156 −.0186  1-3x 1 0 −1.52 −.0561 −.0255−.212 .00157 .0125 .0884 −.0314 −.163 −.0181 .0177 .000243  2-21x2 1 0−1.23 −.0604 .0336 −.252 −.00684 .013 .0829 .0362 −.125 −.0213 −.0167−.0232 865-2x2 1 0 −1.3 −.0456 .0409 −.242 −.0086 .0146 .0837 .0455−.133 −.018 −.022 −.0208  2-23x4 1 1 −1.5 −.04 .00436 −.211 −.00322.00833 .0793 .00699 −.158 −.0156 −.00813 −.0131  2-23x3 1 1 −1.58 −.05−.0167 −.196 .00368 .00868 .0656 −.0284 −.17 −.0133 .0283 −.0189

TABLE 8B image outcome addnl_img MSD  1-4x2 0 0 .2344088 865-16x2 0 0.3205591  1-7x 0 0 .4166765  1-27x2 0 0 .5451415  1-5x2 0 0 .6737114 1-9x 0 0 .6988295 865-1x2 0 0 .754262  1-2x 0 0 .8997956  2-20x2 0 0.9745739 865-10x2 0 0 1.254639 865-15x2 0 0 1.461579  1-12x1 0 01.475262  1-29x2 0 0 1.539608 865-18x2 0 0 1.55325 865-12x2 0 0 1.592335 1-6x2 0 0 1.852124 865-6x2 0 0 2.085814 865-11x2 0 0 2.172602 865-7x2 00 2.193894 865-9x2 0 0 2.736778  1-30x2 0 0 3.718682  1-13x1 0 04.124299 865-3xx2 0 0 4.127046 865-8x2 0 0 4.665785 865-14x2 0 04.835767  2-18x2 0 0 5.822632  1-28x2 0 0 6.094747  2-19x2 0 0 6.130581 1-1x 0 0 6.765957  1-11x2 0 0 6.807669  1-14x 0 0 7.025152  1-10x2 0 07.445808 865-11xx2 0 1 2.216359 865-13xx2 0 1 2.848514  1-12x2 0 12.850898  1-14x2 0 1 2.998304  1-14x3 0 1 4.574026  1-12x3 0 1 7.325252 2-23x2 1 0 2.310336 865-17x2 1 0 2.346174 865-5x2 1 0 2.474172  1-3x 10 10.08229  2-21x2 1 0 12.61865 865-2x2 1 0 15.38419  2-23x4 1 1.6735099  2-23x3 1 1 4.372908

In this embodiment of the present invention, only the scoringcoefficients, means and standard deviations of the coefficients fromTABLE 5 and the standard deviations of the PC variables in TABLE 7 wouldhave to be stored in the computing device for the statistical comparisonanalysis of a particular patient to be conducted. This would preservestorage resources in the event of reference data based on very largepopulations.

Several embodiments of the invention are specifically illustrated and/ordescribed herein. However, it will be appreciated that modifications andvariations of the invention are covered by the above teachings andwithin the purview of the appended claims without departing from thespirit and intended scope of the invention.

1. A computer-implemented method for utilizing shape analysis to assessfetal abnormality, comprising: receiving coordinates of pointsidentifying a shape in a fetal image; determining coefficients of one ormore mathematical functions that describe the identified shape; andutilizing the determined coefficients as markers to assess fetalabnormality.
 2. The method of claim 1, wherein the fetal abnormality isa chromosomal abnormality.
 3. The method of claim 2, wherein thechromosomal abnormality is Down syndrome.
 4. The method of claim 1,wherein the fetal abnormality is Spina Bifida.
 5. The method of claim 1,wherein the points are placed upon a computer monitor.
 6. The method ofclaim 1, wherein the points are placed upon a 3D ultrasound image. 7.The method of claim 1, wherein the points trace an outline around a partof the fetal image to be analyzed.
 8. The method of claim 1, wherein thecoefficients are determined by a Fourier analysis.
 9. The method ofclaim 1, wherein the coefficients are determined by a shape analysismethod selected from the group consisting of elliptical Fourieranalysis, polynomials, cubic splines, parametric polynomials, parametriccubic splines, bezier curves, Fourier analysis of equally spaced radiiand dual axis Fourier analysis.
 10. The method of claim 1, wherein thedetermined coefficients are utilized as markers to assess fetalabnormality in the first trimester.
 11. The method of claim 1, whereinutilizing the determined coefficients as markers comprises conducting astatistical analysis on the determined coefficients.
 12. The method ofclaim 11, wherein the statistical analysis compares the determinedcoefficients with reference parameters derived from a statisticaldistribution of determined coefficients in the unaffected populationand/or affected population.
 13. The method of claim 12, wherein theconducted statistical analysis on the determined coefficients includesat least one of a means calculation, a standard deviation calculationand a correlation calculation.
 14. The method of claim 12, wherein theconducted statistical analysis on the determined coefficients includes aprincipal component analysis.
 15. The method of claim 12, wherein theconducted statistical analysis results in an indication of risk of fetalabnormality.
 16. The method of claim 12, wherein the conductedstatistical analysis results in a likelihood ratio for a fetalabnormality.
 17. The method of claim 12, wherein the conductedstatistical analysis results in an index value to be considered withinrange or outside of range for a fetal abnormality.
 18. The method ofclaim 1, comprising utilizing the determined coefficients as markers incombination with one or more additional markers to assess fetalabnormality.
 19. The method of claim 18, wherein the one or moreadditional markers includes at least one biochemical marker selectedfrom the group consisting of free Beta hCG and PAPP-A, maternal bloodalpha-fetoprotein, maternal blood hCG, maternal blood unconjugatedestriol, maternal blood dimeric inhibin A, maternal urine total estriol,maternal urine beta core fragment, maternal urine hyperglycosylated hCGand maternal blood hyperglycosylated hCG.
 20. The method of claim 18,wherein the one or more additional markers includes at least oneultrasound marker selected from the group consisting of nuchaltranslucency, Ductus Venosus, absent or hypoplastic nasal bone, nuchaledema, short femur, hyperechogenic bowel and echogenic foci in theheart.
 21. The method of claim 1, further comprising: adjusting thereceived coordinates to align the shape according to a particular axisbefore the coefficients are determined.
 22. The method of claim 1,further comprising: adjusting the received coordinates before thecoefficients are determined by at least one of translating thecoordinates, rotating the coordinates and scaling the coordinates. 23.The method of claim 22, wherein utilizing the determined coefficients asmarkers comprises conducting a statistical analysis on the determinedcoefficients.
 24. The method of claim 23, wherein the statisticalanalysis compares the determined coefficients with reference parametersderived from a statistical distribution of determined coefficients inthe unaffected population and/or affected population.
 25. An apparatusfor utilizing shape analysis to assess fetal abnormality, comprising: aprocessor; and a memory storing instructions adapted to be executed bysaid processor to: receive coordinates of points identifying a shape ina fetal image; determine coefficients of one or more mathematicalfunctions that describe the identified shape; and utilize the determinedcoefficients as markers to assess fetal abnormality.
 26. A system forutilizing shape analysis to assess fetal abnormality, comprising: ameans for receiving coordinates of points identifying a shape in a fetalimage; a means for determining coefficients of one or more mathematicalfunctions that describe the identified shape; and a means for utilizingthe determined coefficients as markers to assess fetal abnormality.